Insider Brief
- PhaseV, specializing in causal machine learning for clinical trial optimization, has secured $15 million in funding, led by Viola Ventures and Exor Ventures, with participation from LionBird and angel investors.
- Its proprietary ML technology assists clinical teams in analyzing, designing, and adapting studies in real-time, aiming to expedite drug development, increase certainty, and enhance trial success rates.
- The funding will be used to advance PhaseV’s platform and expand its services to more pharmaceutical, CRO, and biotech companies in Europe and the U.S., aiding in the discovery of new treatments.
PRESS RELEASE — BOSTON/October 24, 2023 /PRNewswire/ — PhaseV a pioneer in causal machine learning (ML) technology that optimizes clinical trial design and analysis, announced today that it has raised $15 million in funding, led by Viola Ventures and Exor Ventures, including participation from LionBird and a group of prominent angel investors.
A recent Deloitte study estimates the average cost of developing a single new drug at $2.3 billion in 2022, with an average 7.1 year deployment time. Moreover, the vast majority of drug candidates do not reach the finish line, and many fail the clinical phase even though the biology works. PhaseV tackles this challenge by leveraging proprietary ML technology that provides clinical development teams an advanced ability to retrospectively analyze and optimally design studies, as well as adapt in real-time throughout the trial. This ML-driven adaptive process can significantly accelerate the clinical drug development process and increase certainty along the way, resulting in more efficient, targeted, and ultimately more successful clinical trials.
“Clinical trials are the most time-consuming and costly stages of drug development, and many trials fail due to inherent uncertainties and complexities in trial design and execution,” said Noam Ohana, Managing Director at Exor Ventures, “PhaseV has demonstrated its technological prowess and commitment to reshaping the landscape of clinical trial design and analysis to ensure that promising drugs reach their full potential.”
“Our platform excels at uncovering hidden signals in clinical and real world data, enabling us to analyze a multitude of covariates using causal-ML to evaluate the most advantageous next steps and decisions in the clinical development process,” said Dr. Raviv Pryluk, CEO and co-founder of PhaseV. “This investment provides us with the resources to further advance our platform and expand our reach to additional pharma, CRO and biotech companies in Europe and the U.S., bringing new needed treatments to patients.”
PhaseV offers two distinct service lines for AI clinical trial optimization. The first includes assessing the potential impact of adaptive trial design on the proposed study, followed by optimal design and execution. The second involves retrospective analysis that detects hidden signals in clinical trial data and evaluates endpoints and subpopulations to redefine success or failure of a trial. Leveraging a wide range of parameters, this multifaceted approach is also valuable for drug repurposing efforts. The company’s approach has proven valuable in a variety of therapeutic areas including oncology, endocrinology, autoimmune diseases, rare diseases and more.
Oramed Pharmaceuticals Inc. (NASDAQ: ORMP), a protein oral drug platform, is one company that has utilized PhaseV’s retrospective services.
“PhaseV played a vital role in the analysis of our Phase 3 trial in the US, ORA-D-013–1, for the treatment of type 2 diabetes. The analysis found subpopulations of patients that responded very well to oral insulin,” said Dr. Miriam Kidron, CSO at Oramed.
“Over the last decade, new technologies have disrupted the way drugs are discovered, but unfortunately the drug development and clinical trial process has largely lagged behind,” said Zvika Orron, general partner at Viola Ventures. “With precision medicine widely acknowledged as the next frontier, drug development stands to gain tremendously from personalized technology capabilities that have proven successful in other industries. We are excited by the promise of PhaseV, combining advanced algorithmic capabilities with drug development expertise to facilitate an important change in the pharma and biotech industries.”
PhaseV was founded by Elad Berkman and Dr. Raviv Pryluk, both graduates of the top technological and academic programs in Israel, with strong track records of successfully collaborating on seemingly unsolvable challenges. Prior to founding PhaseV, they managed large multidisciplinary groups in leading tech companies, with practical experience implementing advanced machine learning, causal inference, and software development at scale. Dr. Dan Goldstaub, who previously managed clinical trials at Teva and MSD, joined the company as a scientific co-founder.
PhaseV’s scientific advisory board includes leading figures in the medical and biostatistics community, who specialize in new methods and advancements in adaptive clinical trials, as well as big pharma and biotech veterans. Dr. Brad Carlin, author of Bayesian Adaptive Methods for Clinical Trials, and Dr. Sofia Vilar, a leading researcher of adaptive clinical trial optimization at the University of Cambridge, are among them, together with Dr. Murray B. Urowitz, Dr. David Perry, Dr. Marcia Levenstein, and Dr. Howard Trachtman.
About PhaseV
Leveraging the power of advanced causal inference and pushing the boundaries of ML, PhaseV detects hidden signals in clinical data and extracts actionable insights for planning the optimal next steps. The company’s technology enables optimal design and closed-loop execution of adaptive clinical trials, increasing efficiency and success rates. PhaseV is advancing paradigm shifts in the clinical trial world in order to bring new treatments to more patients, in a more precise and efficient way. Learn more at www.phaseVtrials.com and follow us on LinkedIn.
Media Contact:
FINN Partners for PhaseV
Aviva Sapir
aviva.sapir@finnpartners.com929–588–2014
Featured image: Credit: PhaseV
